High power and low critical current density spin transfer torque nano-oscillators using MgO barriers with intermediate thickness

Costa, J. D.; Serrano-Guisan, S.; Lacoste, B.; Jenkins, Alex; Böhnert, Tim; Tarequzzaman, M.; Borme, Jérôme; Deepak, Francis Leonard; Paz, Elvira; Ventura, J.; Ferreira, Ricardo; Freitas, P. P.

doi:10.1038/s41598-017-07762-z

Cited by 44 publications

(32 citation statements)

References 42 publications

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“…The thin free layer leads to a low critical current density according to Eq. 1 26 , and the MgO barrier thickness is in an intermediate range with a large TMR, which also contributes to decrease the critical current density as reported recently 25 .…”

Section: Resultssupporting

confidence: 59%

Spin torque nano-oscillator driven by combined spin injection from tunneling and spin Hall current

et al. 2019

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In this paper, a 3-terminal spin-transfer torque nano-oscillator (STNO) is studied using the concurrent spin injection of a spin-polarized tunneling current and a spin Hall current exciting the free layer into dynamic regimes beyond what is achieved by each individual mechanism. The pure spin injection is capable of inducing oscillations in the absence of charge currents effectively reducing the critical tunneling current to zero. This reduction of the critical charge currents can improve the endurance of both STNOs and non-volatile magnetic memories (MRAM) devices.It is shown that the system response can be described in terms of an injected spin current density Js which results from the contribution of both spin injection mechanisms, with the tunneling current polarization p and the spin Hall angle θ acting as key parameters determining the efficiency of each injection mechanism. The experimental data exhibits an excellent agreement with this model which can be used to quantitatively predict the critical points (Js = -2.26±0.09 × 10 9 ħ/e A/m 2 ) and the oscillation amplitude as a function of the input currents. In addition, the fitting of the data also allows an independent confirmation of the values estimated for the spin Hall angle and tunneling current polarization as well as the extraction of the damping α = 0.01 and non-linear damping Q = 3.8±0.3 parameters. Index Terms-Spin Hall Effect, Spin Torque Nano-oscillator, Magnetic Tunnel Junctions.Recent reports demonstrate that the Spin Hall Effect (SHE) can be used to generate pure spin currents, capable of exerting a spin transfer torque (STT) that induces oscillations in a ferromagnetic layer 1,2 . This pure spin current is created by a charge current in a nonmagnetic material with strong spin-orbit coupling where up and down spins are scattered in opposite directions resulting in a spin current orthogonal to the electrical current 2-6 . A central challenge is to quantify the efficiency of the charge current to spin current conversion, which results from the difficulty of measuring spin currents. The spin-orbit material is characterized by a material property called the spin Hall angle, which quantifies the ratio between the generated spin current density ( Hall spin s J ) at an applied charge current density ( Hall spin c J ). The spin Hall angle is expressed as   Hall spin c Hall spin s J J e    with the charge of the electron e and the reduced Plank constant ħ ensuring dimensional consistency. Several techniques have been used to quantify θ of transition metals such as Au, Pd, Pt, Ta, and W. A particularly interesting material is Ta since it is a typical cap and seed layer in magnetic tunnel junction (MTJ) devices and in direct contact with the ferromagnetic free layer. The reported θ values of Ta are in a wide range of 1.4% < θ < 15%, primarily due to dependences on the crystalline phase 6-9 . e J J J J J  stripe of Hall spin c J = -73 × 10 9 A/m 2 injects an equivalent Js into the free layer. At this value the spin Hall effect should excite oscill...

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Section: Resultssupporting

confidence: 59%

Spin torque nano-oscillator driven by combined spin injection from tunneling and spin Hall current

et al. 2019

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“…Hence, considering the resistance mismatch between the amplifier and the STNO, the measured output power is only a fraction of actual emitted power of the STNO. In order to eliminate the effect of impedance mismatch, the integrated matched output power (P out ) of each device is calculated as follows (Costa et al, 2017):…”

Section: Experimental Measurementmentioning

confidence: 99%

“…Therefore, 2-3 orders of magnitude performance improvement is expected using MTJ-Memristor NCSs compared with the CMOS-based NCSs. The use of nano-oscillators specified for NCSs, one order of magnitude improvement in performance compared to the use of MTJ neuron, where full switching is used [critical current density: ∼10 6 A/cm 2 (Costa et al, 2017) vs. ∼10 7 A/cm 2 (Fukami et al, 2016)], is expected. Finally, thermally assisting STNOs using laser can improve the power consumption by 40%.…”

Section: Comparison With Cmos-based Ncsmentioning

confidence: 99%

“…In spintronic-based NCSs, magnetic switching in magnetic tunnel junction (MTJ) (Fong et al, 2016) or magnetic oscillation in spin-torque nano-oscillator (STNO) (Yogendra et al, 2015(Yogendra et al, , 2016Kurenkov et al, 2019) is used to mimic neuron firing. While using oscillation of magnetic moment decreases the power consumption by an order of magnitude compared with the magnetic moment switching [critical current: ∼10 6 Acm −2 (Costa et al, 2017) vs. ∼10 −7 Acm −2 (Fukami et al, 2016)], still there is a huge gap between spintronic-based NCSs and the brain in terms of power consumption and speed. This is due to the fact that the traditional way of oscillating the magnetic moment through the bias current consumes high power and it is done at low speeds.…”

Section: Introductionmentioning

confidence: 99%

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LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

et al. 2020

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Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power highdensity NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100 • C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100 • C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100 • C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100 • C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature.

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“…In spintronic-based NCSs, magnetic switching in magnetic tunnel junction (MTJ) [6] or magnetic oscillation in spin torque nano-oscillator (STNO) [7][8] is used to mimic neuron firing. While using oscillation of magnetic moment decreases the power consumption by an order of magnitude compared with magnetic moment switching (critical current: ~10 6 Acm -2 [9] vs ~10 -7 Acm -2 [10]), still there is a huge gap between spintronic-based NCS and brain in terms of power consumption and speed. This is due to high power consumption and low speed of the traditional way of oscillating the magnetic moment through bias current.…”

Section: Introductionmentioning

confidence: 99%

Spin-Torque-Nano-Oscillator based neuromorphic computing assisted by laser

Farkhani

Böhnert

Tarequzzaman

et al. 2019

2019 14th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era (DTIS)

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In spintronic-based neuromorphic computing systems (NCSs), the switching of magnetic moment in a magnetic tunnel junction (MTJ) or magnetic oscillation in spin torque nano-oscillator (STNO) is used to mimic biological neuron firing. Although using STNO reduces the power consumption significantly, still there is a huge gap between the power consumption of spintronic-based NCS and brain due to the high bias current. In this paper, the power consumption of the proposed STNO-based NCS is reduced by thermally assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of STNOs in NCS reduces by 56% by heating them up to 100°C. The total power consumption of the proposed laser assisted oscillation-based NCS (LAO-NCS) is reduced by 46% at 100°C compared with a typical STNO-based NCS at room temperature.

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High power and low critical current density spin transfer torque nano-oscillators using MgO barriers with intermediate thickness

Cited by 44 publications

References 42 publications

Spin torque nano-oscillator driven by combined spin injection from tunneling and spin Hall current

Spin torque nano-oscillator driven by combined spin injection from tunneling and spin Hall current

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

Spin-Torque-Nano-Oscillator based neuromorphic computing assisted by laser

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